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swin-tiny-patch4-window7-224-finetuned-eurosat-kornia

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  1. README.md +11 -6
  2. model.safetensors +1 -1
  3. runs/Jan09_17-01-19_315083aa3976/events.out.tfevents.1704819695.315083aa3976.4375.4 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7727272727272727
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5184
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- - Accuracy: 0.7727
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.67 | 1 | 0.6793 | 0.5 |
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- | No log | 2.0 | 3 | 0.5184 | 0.7727 |
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.5
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6694
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+ - Accuracy: 0.5
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.67 | 1 | 0.5184 | 0.7727 |
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+ | No log | 2.0 | 3 | 0.7192 | 0.4545 |
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+ | No log | 2.67 | 4 | 0.6369 | 0.5455 |
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+ | No log | 4.0 | 6 | 0.7241 | 0.5455 |
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+ | No log | 4.67 | 7 | 0.8012 | 0.4545 |
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+ | No log | 6.0 | 9 | 0.7166 | 0.5 |
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+ | 0.4504 | 6.67 | 10 | 0.6694 | 0.5 |
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  ### Framework versions
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